Numerous electronic technologies such as digital computers, calculators, audio devices, video equipment, and telephone systems facilitate increased productivity and cost reduction in analyzing and communicating data and information in most areas of business, science, education, and entertainment. Frequently, these activities involve communication and storage of large amounts of information and the complexity and costs of networks and systems performing these activities are often immense. As vast networks of numerous devices are interconnected (such as in the Internets of Things (IoT)), there are a number of issues and problems that can adversely impact performance.
One traditional environment involves the processing and creation of content on relatively few servers (referred to as source servers) and delivery of the results to numerous end-use devices or terminals via the internet. The end-use devices typically operate on information in numerous different formats and configurations. For example, image information can be requested in different formats (e.g., JPEG, WebP, etc.) with numerous visual effects (e.g., various resolutions, zoom-in/out, etc.). Traditionally, the end-use devices or terminals often request information in a particular format and configuration. The source servers perform the corresponding processing and forward the results.
In conventional systems, there are significant burdens or responsibilities imposed on the source servers. For example, in conventional systems the source servers must often remain generalized in order to perform numerous different requisite tasks (e.g., direct control functions, image processing, database processing, storage management, etc.). In traditional environments there are often a large number of end-use devices spread out over vast geographical areas. The end-use devices forward numerous requests for immense amounts of information and tend to create long queues at the relatively few source servers. The source servers essentially become bottlenecks or checkpoints that can cause response delays and adversely impact an end-use experience.
Some traditional approaches include content delivery network (CDN) servers in an attempt to improve performance. However, the CDN servers typically are limited in their ability to alleviate the bottleneck problems. For example, conventional CDN servers do not help much for requests that require additional processing, as these requests requiring additional processing must be forwarded to the source servers. Requests for images that are not stored on the CDN servers also must be forwarded to the source servers. Even if the requested image is stored locally on the CDN servers, the CDN servers often utilize file management systems based on logical addressing of relatively small amounts of information that are uniform in size. The conventional file management systems typically involve relatively large amounts of management operations that slow down response time.